Job Recommender System for the Unique Framework of Classification of Occupations in Colombia (CUOC) using collaborative filtering

Objective: The objective of this research is to improve the matching between labor supply and demand in Colombia by using machine learning techniques and the unique framework of classification of occupations for Colombia (CUOC). This framework allows us to enhance the alignment between resumes and job offers, helping job seekers obtain personalized job recommendations according to their profiles. Methods: This proposal uses a combination of clustering, classification, and collaborative filtering algorithms to obtain the ten best available vacancies for a particular resume. Standardization of resumes and job offers was performed during the preprocessing stage. We utilized natural language algorithms to extract attributes from the CUOC framework. For the training process, we initially employed the K-Means algorithm to group the attributes of the CUOC framework. Afterward, we use KNN, DNN, and AdaBoost as classification algorithms to develop a model that best correlates a resume with the group of vacancies. Finally, a web application was developed using the Django framework, providing a user-friendly interface for job seekers to receive recommendations based on the model outcomes. Results: The best model was selected based on accuracy and processing time. The results indicate that the highest accuracy and recommendation performance are achieved using the CUOC framework, generating a recommendation of the top 10 vacancies based on their similarity level.